U.S. patent number 11,132,632 [Application Number 16/538,566] was granted by the patent office on 2021-09-28 for system and method of automotive production planning.
This patent grant is currently assigned to Blue Yonder Group, Inc.. The grantee listed for this patent is JDA Software Group, Inc.. Invention is credited to Marc Brisson, Vincent Raymond.
United States Patent |
11,132,632 |
Raymond , et al. |
September 28, 2021 |
System and method of automotive production planning
Abstract
A system and method are disclosed including a production planner
that receives a sales forecast for configurations of an automobile.
The demand planner also receives constraints associated with an
automobile supply chain. The demand planner further models
configurations and constraints as a mixed integer linear
programming problem, determines a production plan for automobiles,
and sends instructions to cause automated machinery to retrieve an
amount of automobiles equal to a forecasted production level minus
a current inventory level and to move the amount of the automobile
to an inventory location of the automobile.
Inventors: |
Raymond; Vincent (Montreal,
CA), Brisson; Marc (Boucherville, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
JDA Software Group, Inc. |
Scottsdale |
AZ |
US |
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Assignee: |
Blue Yonder Group, Inc.
(Scottsdale, AZ)
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Family
ID: |
67543900 |
Appl.
No.: |
16/538,566 |
Filed: |
August 12, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20190362286 A1 |
Nov 28, 2019 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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15621525 |
Jun 13, 2017 |
10380524 |
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62361118 |
Jul 12, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
10/067 (20130101); G06Q 10/06313 (20130101); G06Q
10/087 (20130101); G06Q 10/06315 (20130101); G06Q
30/0202 (20130101) |
Current International
Class: |
G06Q
10/06 (20120101); G06Q 10/08 (20120101); G06Q
30/02 (20120101) |
Field of
Search: |
;705/7.22,7.25,7.31,7.37,26.5,28 ;700/100 ;711/165 ;707/E17.01 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Goyea; Olusegun
Attorney, Agent or Firm: Spencer Fane LLP Laureanti; Steven
J.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser.
No. 15/621,525, filed on Jun. 13, 2017, entitled "System and Method
of Automated Production Planning," which claims the benefit under
35 U.S.C. .sctn. 119(e) to U.S. Provisional Application No.
62/361,118, filed Jul. 12, 2016, and entitled "System and Method of
Automated Production Planning." U.S. patent application Ser. No.
15/621,525 and U.S. Provisional Application No. 62/361,118 are
assigned to the assignee of the present application. The subject
matter disclosed in U.S. patent application Ser. No. 15/621,525 and
U.S. Provisional Application No. 62/361,118 is hereby incorporated
by reference into the present disclosure as if fully set forth
herein.
Claims
What is claimed is:
1. A method, comprising: modeling two or more predefined automobile
configurations and one or more constraints associated with an
automobile supply chain as a mixed integer linear programming
problem; determining a production plan for the automobile based, at
least in part, on the two or more predefined automobile
configurations; and comparing the difference between a current
inventory level of an automobile and a forecasted production level
for the automobile in the production plan and sending, by
production planner to automated machinery, instructions to cause
the automated machinery to retrieve an amount of the automobile
equal to the forecasted production level minus the current
inventory level and to move the amount of the automobile to an
inventory location of the automobile.
2. The method of claim 1, further comprising: representing the one
or more predefined automobile configurations by an alphanumeric
string; and aggregating variables of the predefined automobile
configurations by one or more option definition sets.
3. The method of claim 2, wherein the one or more constraints
comprise one or more option production constraints related to the
capability of one or more manufacturers and one or more production
constraints.
4. The method of claim 3, wherein the one or more production
constraints comprises production capacity for one or more
options.
5. The method of claim 4, wherein the mixed integer linear
programming problem comprises a well-structured mixed integer
linear programming problem comprising a network production
sub-model, an option capacity sub-model; and the network production
sub-model is linked to the option capacity sub-model by a linking
constraint sub-model.
6. The method of claim 4, wherein the mixed integer linear
programming problem comprises a mixed integer linear programming
problem model with variable aggregation.
7. A system, comprising: a production planner comprising a
processor and a memory, the production planner configured to: model
two or more predefined automobile configurations and one or more
constraints associated with an automobile supply chain as a mixed
integer linear programming problem; determine a production plan for
the automobile based, at least in part, on the two or more
predefined automobile configurations; and compare the difference
between a current inventory level of an automobile and a forecasted
production level for the automobile in the production plan and send
to automated machinery, instructions to cause the automated
machinery to retrieve an amount of the automobile equal to the
forecasted production level minus the current inventory level and
to move the amount of the automobile to an inventory location of
the automobile.
8. The system of claim 7, wherein the production planner is further
configured to: represent the one or more predefined automobile
configurations by an alphanumeric string; and aggregate variables
of the predefined automobile configurations by one or more option
definition sets.
9. The system of claim 8, wherein the one or more constraints
comprise one or more option production constraints related to the
capability of one or more manufacturers and one or more production
constraints.
10. The system of claim 9, wherein the one or more production
constraints comprises production capacity for one or more
options.
11. The system of claim 10, wherein the mixed integer linear
programming problem comprises a well-structured mixed integer
linear programming problem comprising a network production
sub-model, an option capacity sub-model; and the network production
sub-model is linked to the option capacity sub-model by a linking
constraint sub-model.
12. The system of claim 10, wherein the mixed integer linear
programming problem comprises a mixed integer linear programming
problem model with variable aggregation.
13. The system of claim 11, wherein the linking constraint
sub-model comprises a constraint that joins the production network
sub-model to the option capacity sub-model by setting as equal to
zero the difference between volume of an automobile model that is
produced at a particular plant for a particular market at a
particular time period equal to the production of an automobile
class at the particular plant at the particular time period, summed
over all markets.
14. A non-transitory computer-readable medium comprising software,
the software when executed configured to: model two or more
predefined automobile configurations and one or more constraints
associated with an automobile supply chain as a mixed integer
linear programming problem; determine a production plan for the
automobile based, at least in part, on the two or more predefined
automobile configurations; and compare the difference between a
current inventory level of an automobile and a forecasted
production level for the automobile in the production plan and send
to automated machinery, instructions to cause the automated
machinery to retrieve an amount of the automobile equal to the
forecasted production level minus the current inventory level and
to move the amount of the automobile to an inventory location of
the automobile.
15. The non-transitory computer-readable medium of claim 14,
wherein the software is further configured to: represent the one or
more predefined automobile configurations by an alphanumeric
string; and aggregate variables of the predefined automobile
configurations by one or more option definition sets.
16. The non-transitory computer-readable medium of claim 15,
wherein the one or more constraints comprise one or more option
production constraints related to the capability of one or more
manufacturers and one or more production constraints.
17. The non-transitory computer-readable medium of claim 16,
wherein the one or more production constraints comprises production
capacity for one or more options.
18. The non-transitory computer-readable medium of claim 17,
wherein the mixed integer linear programming problem comprises a
well-structured mixed integer linear programming problem comprising
a network production sub-model, an option capacity sub-model; and
the network production sub-model is linked to the option capacity
sub-model by a linking constraint sub-model.
19. The non-transitory computer-readable medium of claim 17,
wherein the mixed integer linear programming problem comprises a
mixed integer linear programming problem model with variable
aggregation.
20. The non-transitory computer-readable medium of claim 18,
wherein the linking constraint sub-model comprises a constraint
that joins the production network sub-model to the option capacity
sub-model by setting as equal to zero the difference between volume
of an automobile model that is produced at a particular plant for a
particular market at a particular time period equal to the
production of an automobile class at the particular plant at the
particular time period, summed over all markets.
Description
TECHNICAL FIELD
The present disclosure relates generally to automotive production
planning and specifically to a system and method of determining
allocation of automobile configuration production in a multi-plant
multi-market multi-period supply chain.
BACKGROUND
Automobiles (such as cars, trucks, and other types of motorized
vehicles) are typically sold in various configurations. Each
configuration can have hundreds or thousands of different options.
For example, a car may be sold in different trims, such as a sport
model, economy model, premium model, or the like. Each of the
models may have a different type of engine, radio, upholstery,
lighting, or other components. Some of the components may always be
sold together in the same configuration while others may never be
sold in the same configuration. The configurations of the many
components of the typical automobile makes determining an
automotive production plan difficult. The complexity to determine
automobile production with so many configurations is
undesirable.
BRIEF DESCRIPTION OF THE DRAWINGS
A more complete understanding of the present invention may be
derived by referring to the detailed description when considered in
connection with the following illustrative figures. In the figures,
like reference numbers refer to like elements or acts throughout
the figures.
FIG. 1 illustrates an exemplary supply chain network according to a
first embodiment;
FIG. 2 illustrates the production planner of FIG. 1 in greater
detail, in accordance with an embodiment;
FIG. 3 illustrates an exemplary method of automobile configuration
planning according to an embodiment;
FIG. 4 illustrates a graphical representation of a network model of
the automobile supply chain network, according to an
embodiment;
FIG. 5 illustrates the flow of production of a single automobile
model through the exemplary network model of FIG. 4, according to
an embodiment;
FIG. 6 illustrates the flow of production of two automobile models
through the exemplary network model of FIG. 4, according to an
embodiment;
FIG. 7 illustrates the flow of production of two automobile models
with three configurations through the exemplary network model of
FIG. 4, according to an embodiment;
FIG. 8 illustrates a linear equation matrix of the complete
well-structured MIP model of the automotive supply chain network,
according to an embodiment; and
FIG. 9 illustrates the structure of the linear equation matrix of
the complete well-structured MIP model of the automotive supply
chain network, according to an embodiment.
DETAILED DESCRIPTION
Aspects and applications of the invention presented herein are
described below in the drawings and detailed description of the
invention. Unless specifically noted, it is intended that the words
and phrases in the specification and the claims be given their
plain, ordinary, and accustomed meaning to those of ordinary skill
in the applicable arts.
In the following description, and for the purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the various aspects of the invention. It
will be understood, however, by those skilled in the relevant arts,
that the present invention may be practiced without these specific
details. In other instances, known structures and devices are shown
or discussed more generally in order to avoid obscuring the
invention. In many cases, a description of the operation is
sufficient to enable one to implement the various forms of the
invention, particularly when the operation is to be implemented in
software. It should be noted that there are many different and
alternative configurations, devices and technologies to which the
disclosed inventions may be applied. The full scope of the
inventions is not limited to the examples that are described
below.
As described more fully below, aspects of the following disclosure
relate to production planning of automobile configurations in a
multi-plant multi-market multi-period automobile supply chain
network. Automobiles (such as cars, trucks, and other types of
motorized vehicles) are typically sold with the presence or absence
of various components substituted for one another. The presence,
absence, or substitution of any components may be termed as an
"option." A typical automobile may comprise hundreds or thousands
of options, which may be sold as various combinations of options,
or configurations. For example, a particular automobile model may
be sold in various types of trim, such as a sports trim, economy
trim, mid-range trim, premium trim, or the like. Examples of
automobile models are sports utility vehicle (SUV), station wagon,
sedan, coupe, hatchback, electric, and the like. Each of the
automobile models may be associated with available options such as
a specific type of engine (e.g. V8, V6, four cylinder), radio (e.g.
AM/FM radio, satellite radio, touchscreen interface, navigation
equipment), upholstery (e.g. fabric, leather, race-style seating),
lighting (e.g. fog lamps, HID lights, LED lights, projector
headlights), or other like options. Some of the options may be
interdependent such that some options must always be included
together, some options may never be included together, and some
options may or may not be included in the same configuration.
In addition, selecting automobile configurations may be dependent
on more than just the interdependency of options. Selecting such a
configuration may be dependent on demand, capacity and other
manufacturing and logistical constraints, lead time, supply chain
disruption, lot sizes, and other factors. Such factors play a
crucial role in configuration decisions such as adding or removing
options from a configuration or whether to introduce a new
configuration.
The automobile industry has used many techniques to optimize
production planning of automobile configurations including just in
time, build to order, and other techniques. However, embodiments of
the current disclosure solve production planning for automobile
configurations from a higher-level perspective. Some embodiments
employ a network model, a mixed-integer linear problem (MIP) with
variable aggregation, and a well-structured MIP model for
large-scale automotive production planning comprising three
sub-models: a network production sub-model, an option capacity
model, and a linking constraints model. According to embodiments,
production plans based, at least in part, on one or more of these
models may be generated from high-level inputs of automobile,
manufacturing plant, market, and period information.
FIG. 1 illustrates an exemplary supply chain network 100 according
to a first embodiment. Supply chain network 100 comprises
production planner 110, one or more imagers 120, third party
logistics 130, automobile suppliers 140, automobile manufacturers
150, automobile distributors 160, automobile dealerships 170,
computer 180, network 190, and communication links 191-198.
Although a single production planner 110, one or more imagers 120,
one or more third party logistics 130, one or more automobile
suppliers 140, one or more automobile manufacturers 150, one or
more automobile distributors 160, one or more automobile
dealerships 170, a single computer 180, and a single network 190
are shown and described, embodiments contemplate any number of
production planners, imagers, third party logistics, automobile
suppliers, automobile manufacturers, automobile distributors,
automobile dealerships, computers, and networks, according to
particular needs.
In one embodiment, production planner 110 comprises server 112 and
database 114. According to embodiments, production planner 110
receives a demand or production forecast for vehicle model and
options and determines a quantity and configuration of automobiles
to be produced. Production planning may comprise a holistic
approach that considers many or all aspects of a production
planning problem by modeling the production plan as, for example, a
linear program. Production planner 110 may then determine a
quantity and configuration of automobiles to be produced by which
manufacturing plants, for which markets, at which time periods
based on automobile supply chain constraints and possible
automobile configurations.
Automobile supply chain constraints used in the production planning
problem may include, for example, flow constraints (i.e. the number
of automobiles entering a node equals the number of automobiles
leaving a node, or the total of starting stock and production
equals the total of sales and ending stock), manufacturing plant
constraints (e.g. capacity, lead time, and diversity), supplier
capacity constraints, and minimum and maximum stock constraints.
Each automobile configuration may be represented in the production
plan by a serial number, code, or other alphanumeric string
representing one or more, or all, of the possible options for an
automobile configuration.
One or more imagers 120 comprise one or more electronic devices
that receive imaging information from one or more sensors 126 or
from one or more databases in supply chain network 100. According
to embodiments, one or more imagers 120 comprise one or more
processors 122, memory 124, one or more sensors 126, and may
include any suitable input device, output device, fixed or
removable computer-readable storage media, or the like. According
to embodiments, one or more imagers 120 identify items near the one
or more sensors 126 and generate a mapping of the item in supply
chain network 100. As explained in more detail below, one or more
third party logistics 130, suppliers 140, manufacturers 150,
distributors 160, and dealerships 170 use the mapping of an item to
locate the item in the supply chain network 100. The location of
the item is then used to coordinate the storage and transportation
of items in supply chain network 100 to implement one or more plans
generated by production planner 110. Plans may comprise one or more
of a production plan, demand plan, option plan, sales and operation
plan, and master plan, as described herein.
One or more imagers 120 may comprise a mobile handheld device such
as, for example, a smartphone, a tablet computer, a wireless
device, or the like. In addition, or as an alternative, one or more
imagers 120 comprise one or more networked electronic devices
configured to transmit item identity information to one or more
databases as an item passes by or is scanned by one or more imagers
120. This may include, for example, a stationary scanner located at
one or more third party logistics 130, suppliers 140, manufacturers
150, distributors 160, or dealerships 170 that identifies items as
the items pass near the scanner, including in one or more
transportation vehicles 136. One or more sensors 126 of one or more
imagers 120 may comprise an imaging sensor, such as, a camera,
scanner, electronic eye, photodiode, charged coupled device (CCD),
or any other electronic or manual sensor that detects or identifies
images of automobiles or automotive components or reads labels,
barcodes, or the like coupled with automobiles or automotive
components. In addition, or as an alternative, one or more sensors
126 may comprise a radio receiver and/or transmitter configured to
read an electronic tag coupled with an automobile or automotive
component, such as, for example, an RFID tag.
Third party logistics 130 comprises server 132 and database 134.
According to embodiments, third party logistics 130 directs one or
more transportation vehicles 136 to ship one or more items between
one or more third party logistics 130, suppliers 140, manufacturers
150, distributors 160, or dealerships 170, based, at least in part,
on the quantities of a production plan determined by production
planner 110. Transportation vehicles 136 comprise, for example, any
number of trucks, cars, vans, boats, airplanes, unmanned aerial
vehicles (UAVs), cranes, robotic machinery, or the like. In
addition to the production plan, the number of items shipped by
transportation vehicles 136 in third party logistics 130 may also
be based, at least in part, on the number of items currently in
stock at one or more third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, or dealerships 170, the number
of items currently in transit, forecasted demand, a supply chain
disruption, and the like. According to embodiments, transportation
vehicles 136 may be associated with one or more suppliers 140,
manufacturers 150, distributors 160, or dealerships 170, or another
supply chain entity, according to particular needs.
As shown in FIG. 1, supply chain network 100 operates on one or
more computers 180 that are integral to or separate from the
hardware and/or software that support production planner 110 and
one or more imagers 120, third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, and dealerships 170. Supply
chain network 100 comprising production planner 110 and one or more
imagers 120, third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, and dealerships 170 may
operate on one or more computers 180 that are integral to or
separate from the hardware and/or software that support the
production planner 110 and one or more imagers 120, third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
and dealerships 170. Computers 180 may include any suitable input
device 182, such as a keypad, mouse, touch screen, microphone, or
other device to input information. Output device 184 may convey
information associated with the operation of supply chain network
100, including digital or analog data, visual information, or audio
information.
Computer 180 may include fixed or removable computer-readable
storage media, including a non-transitory computer readable medium,
magnetic computer disks, flash drives, CD-ROM, in-memory device or
other suitable media to receive output from and provide input to
supply chain network 100. Computer 180 may include one or more
processors 186 and associated memory to execute instructions and
manipulate information according to the operation of supply chain
network 100 and any of the methods described herein. In addition,
or as an alternative, embodiments contemplate executing the
instructions on computer 180 that cause computer 180 to perform
functions of the method. Further examples may also include articles
of manufacture including tangible computer-readable media that have
computer-readable instructions encoded thereon, and the
instructions may comprise instructions to perform functions of the
methods described herein. According to some embodiments, the
functions and methods described in connection with imager 120 may
be emulated by one or more modules configured to perform the
functions and methods as described.
Production planner 110 and one or more imagers 120, third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
and dealerships 170 may each operate on one or more separate
computers, a network of one or more separate or collective
computers, or may operate on one or more shared computers. In
addition, supply chain network 100 may comprise a cloud based
computing system having processing and storage devices at one or
more locations, local to, or remote from production planner 110 and
one or more imagers 120, third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, and dealerships 170. In
addition, each of the one or more computers 180 may be a work
station, personal computer (PC), network computer, notebook
computer, tablet, personal digital assistant (PDA), cell phone,
telephone, smartphone, mobile device, wireless data port, augmented
or virtual reality headset, or any other suitable computing device.
In an embodiment, one or more users may be associated with
production planner 110 and one or more imagers 120, third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
and dealerships 170. These one or more users may include, for
example, a "manager" or a "planner" handling production planning
and/or one or more related tasks within supply chain network 100.
In addition, or as an alternative, these one or more users within
supply chain network 100 may include, for example, one or more
computers programmed to autonomously handle, among other things,
production planning, demand planning, option planning, sales and
operations planning, order placement, automated warehouse
operations (including removing automobile components from and
placing automobile components in inventory), robotic production
machinery (including building or assembling automobiles or
automobile components), and/or one or more related tasks within
supply chain network 100.
One or more third party logistics 130, suppliers 140, manufacturers
150, distributors 160, and dealerships 170 represent one or more
automotive supply chain entities in one or more supply chain
networks 100, including one or more enterprises. One or more third
party logistics 130 may be any suitable entity that provides
warehousing and transportation for automobile or automotive
components in the automobile supply chain. Third party logistics
130 may, for example, receive an automobile or automotive component
from a supply chain entity in the supply chain network and store
and transport the automobile or automotive component for another
supply chain entity. One or more third party logistics 130 may
comprise automated warehousing systems that automatically remove
automobile components from and place automobile components into
inventory based, at least in part, on a production plan, demand
plan, option plan, sales and operation plan, or master plan
determined by production planner 110. Automotive components may
comprise, for example, components, materials, products, parts,
items, or supplies that may be used to produce automobiles or other
automotive components. In addition, or as an alternative, an
automotive component may comprise a part of the automobile or a
supply or resource that is used to manufacture the automobile, but
does not become a part of the automobile.
One or more suppliers 140 may be any suitable entity that offers to
sell or otherwise provides one or more automotive components to one
or more automotive manufacturers 150. Suppliers 140 may comprise
automated distribution systems 142 that automatically transport
automobiles and automotive components to one or more automotive
manufacturers 150 based, at least in part, on a production plan,
demand plan, option plan, sales and operation plan, or master plan
determined by production planner 110 and/or one or more other
factors described herein.
Automobile manufacturer 150 may be any suitable entity that
manufactures at least one automobile or automotive component.
Manufacturer 150 may use one or more automotive components during
the manufacturing process to manufacture, fabricate, assemble, or
otherwise process an automobile or automotive component. An
automobile or automotive component may be supplied to another
automobile manufacturer 150, third party logistics 130, supplier
140, distributor 160, and/or dealership 170 in the automobile
supply chain network 110. Automobile manufacturer 150 may, for
example, produce and sell an automobile or automotive component to
supplier 140, another manufacturer 150, a distributor 160,
dealership, 170 a customer, or any other suitable person or entity.
Such automobile manufacturers 150 may comprise automated robotic
production machinery 152 that produces automobiles and automobile
components based, at least in part, on a production plan, demand
plan, option plan, sales and operation plan, or master plan
determined by production planner 110.
Distributor 160 may be any suitable entity that offers to sell or
otherwise distributes at least one automobile or automotive
component to one or more dealerships 170 and/or customers.
Distributors 160 may, for example, receive a product from a first
automotive supply chain entity in supply chain network 100 and
store and transport the product for a second automotive supply
chain entity. Such distributors 160 may comprise automated
warehousing systems 162 that automatically transport to one or more
dealerships 170 or customers and/or automatically remove from or
place into inventory automobiles and automobile components based,
based, at least in part, on a production plan, demand plan, option
plan, sales and operation plan, or master plan determined by
production planner 110. One or more dealerships 170 may be any
suitable entity that obtains one or more automobiles or automotive
component to sell to one or more customers. In addition, one or
more dealerships 170 may sell, store, and supply one or more
automotive components and/or repair an automobile with one or more
automotive components. One or more dealerships 170 may comprise any
online or brick and mortar location, including locations with
shelving systems 172. Shelving systems 172 may comprise, for
example, various racks, fixtures, brackets, notches, grooves,
slots, or other attachment devices for fixing shelves in various
configurations. These configurations may comprise shelving with
adjustable lengths, heights, and other arrangements, which may be
adjusted by an employee of one or more dealerships 170 based on
computer-generated instructions or automatically by machinery to
place automobiles or automotive components in a desired
location.
Although one or more third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, and dealerships 170 are shown
and described as separate and distinct entities, the same entity
may simultaneously act as any one or more third party logistics
130, suppliers 140, manufacturers 150, distributors 160, and
dealerships 170. For example, one or more automobile manufacturers
150 acting as a manufacturer could produce an automobile or
automotive component, and the same entity could act as a supplier
to supply an automobile or automotive component to another
automotive supply chain entity. Although one example of a supply
chain network 100 is shown and described; embodiments contemplate
any configuration of supply chain network 100, without departing
from the scope of the present disclosure.
In one embodiment, production planner 110 may be coupled with
network 190 using communications link 191, which may be any
wireline, wireless, or other link suitable to support data
communications between production planner 110 and network 190
during operation of supply chain network 100. One or more imagers
120 are coupled with network 190 using communications link 192,
which may be any wireline, wireless, or other link suitable to
support data communications between one or more imagers 120 and
network 190 during operation of distributed supply chain network
100. Third party logistics 130 may be coupled with network 190
using communications link 193, which may be any wireline, wireless,
or other link suitable to support data communications between third
party logistics 130 and network 190 during operation of supply
chain network 100.
One or more suppliers 140 may be coupled with network 190 using
communications link 194, which may be any wireline, wireless, or
other link suitable to support data communications between one or
more suppliers 140 and network 190 during operation of supply chain
network 100. One or more manufacturers 150 may be coupled with
network 190 using communications link 195, which may be any
wireline, wireless, or other link suitable to support data
communications between one or more manufacturers 150 and network
190 during operation of supply chain network 100. One or more
distributors 160 may be coupled with network 190 using
communications link 196 which may be any wireline, wireless, or
other link suitable to support data communications between one or
more distributors 160 and network 190 during operation of supply
chain network 100. One or more dealerships 170 may be coupled with
network 190 using communications link 197, which may be any
wireline, wireless, or other link suitable to support data
communications between one or more dealerships 170 and network 190
during operation of supply chain network 100. Computer 180 may be
coupled with network 190 using communications link 198, which may
be any wireline, wireless, or other link suitable to support data
communications between computer 180 and network 190 during
operation of supply chain network 100.
Although communication links 191-198 are shown as generally
coupling production planner 110 and one or more imagers 120, third
party logistics 130, suppliers 140, manufacturers 150, distributors
160, dealerships 170, and computer 180 to network 190, each of
production planner 110 and one or more imagers 120, third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
dealerships 170, and computer 180 may communicate directly with
each other, according to particular needs.
In another embodiment, network 190 includes the Internet and any
appropriate local area networks (LANs), metropolitan area networks
(MANs), or wide area networks (WANs) coupling production planner
110 and one or more imagers 120, third party logistics 130,
suppliers 140, manufacturers 150, distributors 160, dealerships
170, and computer 180. For example, data may be maintained by
locally or externally of production planner 110 and one or more
imagers 120, third party logistics 130, suppliers 140,
manufacturers 150, distributors 160, dealerships 170, and computer
180 and made available to one or more associated users of
production planner 110 and one or more imagers 120, third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
dealerships 170, and computer 180 using network 190 or in any other
appropriate manner. For example, data may be maintained in a cloud
database at one or more locations external to production planner
110 and one or more imagers 120, third party logistics 130,
suppliers 140, manufacturers 150, distributors 160, dealerships
170, and computer 180 and made available to one or more associated
users of production planner 110 and one or more imagers 120, third
party logistics 130, suppliers 140, manufacturers 150, distributors
160, dealerships 170, and computer 180 using the cloud or in any
other appropriate manner. Those skilled in the art will recognize
that the complete structure and operation of network 190 and other
components within supply chain network 100 are not depicted or
described. Embodiments may be employed in conjunction with known
communications networks and other components.
In accordance with the principles of embodiments described herein,
production planner 110 and/or one or more automotive supply chain
entities may generate one or more supply chain plans, including a
production plan, a demand plan, an option plan, a sales and
operation plan, and a master plan, and modify the supply chain
based on the generated plans. For example, according to some
embodiments, production planner 110 automatically places orders for
automobile or automotive components at one or more suppliers 140,
manufacturers 150, or distributors 160, initiates manufacturing of
the automobile or automotive components at one or more
manufacturers 150, and determines automobile or automotive
components to be carried at one or more dealerships 170.
Furthermore, production planner 110 may instruct automated
machinery (i.e., robotic warehouse systems, robotic inventory
systems, automated guided vehicles, mobile racking units, automated
robotic production machinery, robotic devices and the like) to
adjust product mix ratios, inventory levels at various stocking
points, production of products of manufacturing equipment, and
proportional or alternative sourcing of one or more third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
and dealerships 170 based on one or more generated plans and/or
current inventory or production levels. For example, the methods
described herein may include computers 180 receiving product data
from automated machinery having at least one sensor and the product
data corresponding to an item detected by the automated machinery.
The received product data may include an image of the item, an
identifier, as described above, and/or other product data
associated with the automobile or automotive component (dimensions,
texture, estimated weight, and any other like attributes). The
method may further include computers 180 looking up the received
product data in database 114 associated with production planner 110
to identify the item corresponding to the product data received
from the automated machinery.
Computers 180 may also receive, from the automated machinery, a
current location of the identified automobile or automotive
component. Based on the identification of the automobile or
automotive component, computers 180 may also identify (or
alternatively generate) a first mapping in the database system,
where the first mapping is associated with the current location of
the identified automobile or automotive component. Computers 180
may also identify a second mapping in the database system, where
the second mapping is associated with a past location of the
identified automobile or automotive component. Computers 180 may
also compare the first mapping and the second mapping to determine
if the current location of the identified automobile or automotive
component in the first mapping is different than the past location
of the identified automobile or automotive component in the second
mapping. Computers 180 may then send instructions to the automated
machinery based, as least in part, on one or more differences
between the first mapping and the second mapping such as, for
example, to locate automobile or automotive component to add to or
remove from an inventory of one or more third party logistics 130,
suppliers 140, manufacturers 150, distributors 160, and dealerships
170.
FIG. 2 illustrates production planner 110 of FIG. 1 in greater
detail, in accordance with an embodiment. As discussed above,
production planner 110 may comprise server 112 and database 114.
Although production planner 110 is shown and described as
comprising a single server 112 and database 114, embodiments
contemplate any suitable number of servers or databases internal to
or externally coupled with production planner 110. In addition, or
as an alternative, a production planner may be located internal or
external to the one or more third party logistics 130, suppliers
140, manufacturers 150, distributors 160, and dealerships 170
according to particular needs.
According to embodiments, server 112 of production planner 110 may
comprise production planning engine 202, demand engine 204, option
engine 206, sales and operations engine 208, master planner 210,
and modeler 212. In addition, database 114 of production planner
110 comprises a configuration database 220 (which comprises options
data 222, option constraints 224, configuration data 226, and
hierarchy data 228), sales forecast data 230, production capacity
data 232, inventory data 234, time period data 236, manufacturer
data 238, market data 240, option production constraints 242, and
models 244. Although particular engines, planners, modelers, and
databases are shown and described, embodiments contemplate any
suitable number or combination of engines, planners, modelers, and
databases located at one or more locations, local to, or remote
from, production planner 110, according to particular needs.
Furthermore, the engines, planners, modelers, and databases may be
located at one or more locations, local to or remote from,
production planner 110 such as on multiple servers or computers at
any location in the supply chain network, such as networked among
one or more third party logistics 130, suppliers 140, manufacturers
150, distributors 160, and dealerships 170.
Production planning engine 202 of server 112 may determine a
production plan based, at least in part, on one or more constrained
or unconstrained plans received from demand planning engine 204,
option planning engine 206, and/or sales and operations planning
engine 208. For example, production planner 110 may reconcile the
option plan from option engine 206 according to the demand plan
received from demand planning engine 204 and the sales and
operations plan received from sales and operation engine 208,
iteratively, to generate a production plan. In other words,
production planner 110 may receive the demand plan, option plan,
and sales and operations plan and refine each of the plans
iteratively to generate a production plan.
According to embodiments, production planner 110 generates a
production plan based, at least in part, on automobile
configurations (including predefined vehicle configurations, which
may be referred to as "predefined automobiles"), historical and
forecast sales data 230, production capacity data 232, inventory
data 234, time period data 235, manufacturer data 238, market data
240, option production constraints 242, and any other constraints
in accordance with one or more models 244. The predefined
automobiles may comprise, for example, a list of automobile
configurations which may include a demand associated with
particular options, configurations, or fully defined vehicles
(FDV), as described in more detail below. Historical and forecast
sales data 230 may comprise, for example, past and projected demand
of sales organized according to any particular criteria, including
automobile models, automobile options, automobile configurations,
components, automobiles, markets, periods, and the like.
Production capacity data 232 may comprise data establishing the
minimum and maximum capacity for production of one or more
manufacturers 150 for automobile or automobile components over a
given time period and may be associated with a lead time. Inventory
data 234 may comprise the minimum and maximum number of automobiles
models or automobile components that may be stored at various
stocking points in the supply chain as well as the current or
projected stock at each stocking point. According to embodiments,
production planning engine 202 receives and transmits inventory
data 234, including item identifiers, pricing data, attribute data,
inventory levels, and other like data about one or more automobiles
or automotive components between one or more locations in the
supply chain network 100 including among one or more third party
logistics 130, suppliers 140, manufacturers 150, distributors 160,
and dealerships 170.
Time period data 236 may comprise, for example, any suitable time
information, such as a planning period of weeks, months, days,
years, quarter, or any other suitable time period over which one or
more plan is determined. Importantly, time period information may
be especially critical to the functionality of production planner
110 where a production planner may need to consider inputs over a
long time period, such as, for example, between one and two years,
or longer. Manufacturer data 238 may comprise data relating to the
manufacturing plants for automobile or automobile components such
as the markets served by each manufacturer 150, the distribution
chains for each manufacturer 150, and the types of automobile and
automobile components that may be manufactured at each manufacturer
150. Market data 240 may comprise data delineating the regions
(geographic, economic, or otherwise) that are used to model
distribution of automobile or automobile components.
Manufacturer data 238 and market data 240 may comprise, for
example, the number, type, and location of automobile manufacturers
150 and the markets that those manufacturers 150 serve. For
example, manufacturers 150 may be associated with a particular
region where the manufacturer 150 operates, such as, the United
States, Canada, Mexico, Europe, or any other geographical region,
such as a state, country, or continent. Similarly, markets may be
divided into any desired geographical region, such as by state,
country, continent, or any other region. Markets may comprise, for
example, the Americas, Europe, and Asia, or markets may comprise
the United States, Mexico, and Canada. Any other combination of
manufacturing plant and market information may be organized into
any desired region, according to particular needs. Option
production constraints 242 are related to the capability of the
manufacturers 150 to produce options and may comprise production
constraints such as, for example, production capacity for
particular options. Demand planning engine 204 of server 112 may
receive historical and forecast sales data 230, such as, for
example, past and projected demand data and marketplace data from
dealerships 170 and determine a demand plan, based, at least in
part, on the received data.
Option planning engine 206 of server 112 may determine an option
plan by associating constraints with options of automobile option
packages according to options and configurations stored in
configuration database 220. Options data 222 of configuration
database 220 may comprise data identifying available options
associated with the make and models of automobiles. Each option may
be associated with a particular automobile or one or more options
may be associated with one or more automobiles, according to
particular needs. Options may comprise selectable or configurable
features, components, or configurations of automobiles. For
example, options may comprise selection of an engine, transmission,
wheels, color, seats, head lamps, quality of materials (such as
interior or exterior finish options), brakes, tires, intake,
exhaust, spoiler, or other components or systems of an automobile.
Options may comprise the absence or presence of any automotive
component (such as, for example, having a spoiler or not having a
spoiler) or may represent a particular configuration of any
automotive component (such as, for example, having a V8 engine
versus a 4-cylinder engine). Options may comprise a particular
version or part number of a selected automotive component, which
may vary based on geographical location, safety requirements,
ruggedness, value (such as, for example, a premium versus an
economy model), or like considerations.
One or more of the options may have relationships that define
various combinations and permutations of options in a finished
automobile and/or an automotive component. These relationships may
be defined by option constraints 224 of configuration database 220.
Option constraints 224 comprise limits and permissions for
relationships between options, such as limits to which options may
occur together in a configuration and which options may not occur
together in a configuration. Option constraints 224 may require
that certain options are always found in an automobile together,
are never found in an automobile together, are dependent or
independent of other options, must be found in specific ratios in
the automobile, and other like rules and constraints. For example,
option constraints 224 may include, for example, that "premium
leather seating is only available with V-8 engine." Therefore, any
option for premium leather seating would be allowed only if the
option for V-8 engine also occurred in the same configuration.
Embodiments contemplate any suitable option constraints 224,
according to particular needs. Option planning engine 206 may use
options constraints 224 to refine the demand plan of the demand
planning engine, such that, the demand for options is compatible
with supply chain constraints, such as, for example, option
production constraints 244.
In addition, options constraints 224 may be assigned to options
according to a hierarchy stored in hierarchy data 228. Hierarchy
data 228 may comprise a priority associated with each option such
that options with a higher priority are assigned to an automobile
prior to an option with a lower priority. For example, a demand
target for an option for a V8 engine may have a higher priority
than a demand target for an option to include leather seats. If,
for example, the demand for the V8 engine was 100 vehicles and for
leather seats was 80 vehicles, and all vehicles with a V8 engine
must also have leather seats, then, the demand for V8 engines will
have a higher priority than the demand for leather seats.
Accordingly, the production planner 110 may determine that the
production targets would be 100 vehicles with both the V8 engine
option and the leather seats option.
Each combination or permutation of automobile options may be termed
a configuration stored as configuration data 226. A configuration
may comprise any collection of one or more automobile options, such
as particular lighting systems, engines, model type, wheels, or any
component or part of an automobile that may be configured,
including permitted and disallowed configurations of each
automobile.
Sales and operations planning engine 208 of server 112 may
determine a sales and operations plan based, at least in part, on
option production constraints 242. According to embodiments, sales
and operations planning engine 208 receives option production
constraints 242 such as, for example, constraints covering
production limits on select options. For example, production limits
may be maximum supply available per a defined time horizon that is
available to meet a particular demand volume. Embodiments
contemplate that option production constraints 242 are defined by a
combination of logical operators.
According to embodiments, demand planning engine 204 and option
planning engine 206 determine an unconstrained demand plan and
option plan. In one embodiment, sales and operations planning
engine 208 receives an unconstrained demand and option plan as an
input and then constrains the plan based on production limits and
option compatibility. In addition, or as an alternative, the output
of the sales and operations plan may comprise a constrained demand
and option plan which may not equal the unconstrained plan. In
addition, the sales and operations plan may be visible and
applicable to all parts of supply chain network 100.
After the demand plan, option plan, and sales and operations plan
are determined and production planning engine 202 generates a
production plan, master planner 210 of server 112 may generate a
master plan and communicate the master plan to one or more third
party logistics 130, suppliers 140, manufacturers 150, distributors
160, and dealerships 170 to produce automobiles or automotive
components according to the refined master plan. As an example only
and not by way of limitation, master planner 120 may place orders
with one or more third party logistics 130, suppliers 140,
manufacturers 150, and distributors 160 to produce or ship
automobile and automotive components according to the master plan
and may communicate to dealerships 170 the quantity and options of
automobiles and automotive components that will be produced and the
date that the automobiles and automotive components will arrive at
dealerships 170.
According to embodiments, the level of granularity in the master
plan is different than the production plan. Master planning may
comprise, for example, a buffer of an amount of material and an
operation that processes or transforms the material into an item
with a set quantity. Embodiments of production planner 110
determine a production plan comprising a higher level of
granularity than master planning. After the one or more inputs
described above are received by production planner 110, modeler 212
of production planner 110 may determine a production plan utilizing
one or more models 244.
Models 244 of the database may comprise any suitable model of an
automobile supply chain. According to some embodiments, the models
comprise a network model comprising nodes and arcs where nodes
represent manufacturers 150, automobile configurations, and markets
and arcs represent the movement of automobile stock, as described
in more detail below. According to other embodiments, models 244
comprise a mixed integer linear programming MIP model with variable
aggregation. According to further embodiments, models 244 comprise
a well-structured MIP model comprising three sub-models: a network
production sub-model, an option capacity model, and a linking
constraints model.
FIG. 3 illustrates an exemplary method 300 of automobile
configuration planning according to an embodiment. Although
automobile configuration planning is depicted as a linear process,
one or more actions may be performed in any order, combination, or
repetitions to perform automobile configuration planning. For
example, demand planning 302, option planning 304, and sales and
operations planning 306 may comprise iterative processes that are
performed multiple times in various orders, such that the demand
plan, the option plan, and sales and operations plan inform and
refine each other according to sales forecast data 230, production
capacity data 232, inventory data 234, time period data 236,
manufacturer data 238, market data 240, models 232, option
production constraints 244, options data 222, options constraints
224, configuration data 226, and hierarchy data 228. However,
during demand planning 302, option planning 304, and sales and
operations planning 306, the determined plans generally have few
initial constraints, which helps generate plans directed to what
the automobile manufacturer 150 would like to build, not
necessarily what they are able to build. As the planning proceeds
through further actions, more constraints are added or removed to
further align a desired plan with a feasible plan.
At action 302, demand planning engine 204 generates a demand plan
from a global consolidated view of market demand and production
requirements. Demand planning engine 204 may receive historical and
forecast sales data 230, option production constraints 244, and the
like and generate a demand plan, which may include projected demand
for one or more automobiles and automotive components. A demand
plan may include a preliminary assessment of data received from
dealerships 170, such as, for example, demand for types and
quantities of automobiles and automotive components. Production
planner 110 may communicate the generated demand plan to the option
planning engine 206 and sales and operations planning engine
208.
At action 304, option planning engine 206 may determine the take
rates and volumes of automobiles and automotive components at the
option level. Option planning engine 206 may refine the demand plan
according to the mix or the interaction between available
automobile options. After action 304, production planner 110 may
return to action 302 and iteratively refine the demand plan
according to the option plan, such as analyzing the available
options and returning to the demand plan to alter take rate
percentages. In addition, or in the alternative, production planner
110 may continue to action 306. At action 306, sales and operations
planning engine 208 may generate a sales and operation plan
optimized to fulfill market demand and generate forecast orders.
For example, sales and operations planning engine 208 may refine
the option plan according to production capacity data 232,
incrementally, so that, for example, a sales and operation plan is
substantially refined according to the demand plan.
At action 308, production planning engine 202 communicates with
third party logistics 130, suppliers 140, manufacturers 150,
distributors 160, dealerships 170 and/or other automotive supply
chain entities to generate a production plan that is optimized
based on market demand while respecting constraints according to
models 244. The production plan may determine, for example, which
automobiles are to be produced for particular markets, at which
manufacturers 150, for each of one or more time periods. The
production plan may be based on overall sales forecasts and
respects supplier 140 and manufacturer 150 production capacity
constraints. After the one or more inputs described above are
received by production planner 110, modeler 212 of production
planner 110 may determine a production plan utilizing one or more
models 244 as described below.
At action 310, master planner 210 generates a master plan for
production of automobiles and automobile components. For example,
master planner 210 may generate a master plan that determines which
automobiles and automotive components will be produced during a
specific time frame or planning horizon, and the order or priority
of the automobiles and automotive components produced. As discussed
herein, production planning engine 202 generates a production plan
that is optimized based on market demand while respecting
constraints according to models 244. The models described below
include: a network model, a MIP model with variable aggregation,
and a well-structured MIP model.
Models 244 may include one or more constraints. For example, models
244 may include one or more sales forecast constraints, production
capacity constraints, supplier and production capacity constraints
for option definition sets, and the like. A sales forecast
constraint may comprise requiring that the inventory at the end of
the previous planning period plus the production during the current
planning period minus the inventory at the end of the current
planning equals the sales forecast for the current planning period.
The sales from a specific period may be shuffled to obtain what
remains in stock so that the equation may be represented by the
previous stock added to what is produced and subtracting what is
sold is equal to the new stock for a given period. Production
capacity constraints may comprise limiting the number of vehicles
of a vehicle model (sedan or SUV for example) produced at a
manufacturer 150 by the capacity of each plant of the manufacturer
150. Production capacity constraints are defined for each
manufacturing plant, each period, and each vehicle model. Supplier
and production capacity constraints may be defined for option
definition sets (ODS) and for each period. Each of the constraints
in models 244 may be defined by ODS and time period. An ODS
represents a particular automobile sub-configuration (for instance,
a black sedan with a V8).
According to embodiments, ODS comprise rules that target some
options for one or more configurations. For example, some
manufacturers 150 may be restricted for a particular option, such
as a big engine, which limits the amount of production for a
particular configuration comprising that option. ODS match
automotive configurations and options which are represented by a
FDV. By way of further explanation an example is not given.
TABLE-US-00001 TABLE 1 Quantity Market Period Model 100 USA 1 Sedan
97 USA 2 Sedan 90 USA 3 Sedan 100 USA 4 Sedan 50 USA 5 Sedan 65 USA
6 Sedan 120 USA 7 Sedan 100 USA 8 Sedan 110 USA 9 Sedan 102 USA 10
Sedan
TABLE 1 illustrates a sales forecast for a single market for a
single automobile model for ten upcoming time periods. Unlike other
production planners that determine particular options to be
produced, embodiments of the current disclosure generate production
plans based on high-level sales forecasts comprising automobile,
manufacturing plant, market, period information, such as from
marketing or analysis from an automobile manufacturer 150.
According to the example illustrated, a sedan is needed in a
quantity of 100 automobiles for the USA market on period 1. After
the high-level sales forecasts are received by the production
planner, the production planner receives predefined automobiles,
which may comprise, for example, a list of automobile
configurations and a demand associated with particular options,
configurations, or fully defined vehicles (FDV).
TABLE-US-00002 TABLE 2 FDV Sedan V8 RadioA Sunroof Sedan V8 RadioB
Sunroof Sedan V8 RadioA noSunroof Sedan V6 RadioA Sunroof Sedan V6
RadioB noSunroof Sedan V6 RadioC Sunroof
As illustrated in TABLE 2, each of the FDV comprise codes that
precisely define the configuration of each automobile. Each
configuration may be represented by a string of letters and numbers
that identify configuration options such as: automobile model,
engine, radio, lights, color, braking system, or any other like
automobile configuration options. Although the FDVs illustrated in
TABLE 2 include options for model (Sedan), engine (V8/V6), radio
(RadioA, RadioB, and RadioC), and sunroof (Sunroof/No Sunroof),
embodiments contemplate a FDV code comprising any string of text,
numbers, logical operators, or the like that precisely define most
or all of the options present on a vehicle. Additionally, the
production planner may receive sales forecasts associated with FDV,
but, in most instances, the production planner will receive sales
forecasts associated with particular options or combinations of
options, which are defined by ODS.
ODS may be used to associate a FDV to a production option
constraint. This constraint may be, for example, a capacity (upper
bound) or a desired target. For example, in the following TABLE 3,
various ODS have production limits for various weekly time periods,
according to the following:
TABLE-US-00003 TABLE 3 2015/ 2015/ 2015/ 2015/ 2015/ ODS 2015/W36
W37 W38 W39 W40 W41 Black SUV with V8 1327 1164 1406 1300 1406 1300
Any model with V6 4617 4049 4892 4524 4892 4524 Sedan with a
sunroof 1616 911 1100 1017 1100 1017 but without spoiler SUV with a
radio A 1750 1306 1630 1750 1058 1600 All sedans and pickups 652
1000 1000 175 1025 650 SUV with a 4 cylinders 3800 3361 3240 3122
3323 3362
TABLE 3 illustrates ODS associated with the production capacities
for particular time periods. For example, according to TABLE 3, the
ODS "Black SUV with V8" which represents all SUVs that are black
and have a V8 engine, is limited to 1,327 automobiles in the 36th
week of 2015 and to 1,164 automobiles in the 37th week of 2015.
This option definition set represents that, regardless of any
configurations of available options (and many options will fall
under the ODS: Black SUV with V8), the production limit for all
configurations whose FDV matches the ODS in the chart is limited by
the associated production capacity at each listed time period.
By way of a further example, in Row 4, the ODS "SUV with a radio A"
represents one type of automobile and one type of radio. Any FDV
that comprises SUV AND radio A will be associated with the
production capacity constraint that the total of all SUVs with the
radio A is limited to 3,800 automobiles in the 36th week of 2015
and 3,361 in the 37th week of 2015. If, however, the radio A was
used in another type of automobile, its production capacity would
not be limited by the production capacity listed in the chart
because the ODS would not match the FDV associated with that
automobile configuration. Although the production capacity
constraints in TABLE 3 are associated with time periods expressed
in weeks, embodiments contemplate any suitable time period, such as
hours, days, months, quarters, years, or any other suitable period
of time.
According to an embodiment, the number of particular FDV entries
for an automobile manufacturer may exceed 1,000,000 entries. The
large number of entries creates scalability problems for many types
of production planners. Other described mixed integer problem
models may not be scalable to the number of options provided in an
automobile configuration context. According to some embodiments,
production planner 110 uses variable aggregation to limit or reduce
the number of automobile models analyzed by the model by
constructing a novel data structure that limits the number of
automobile configurations. This novel data structure is designed to
improve the way a computer stores and retrieves data in memory.
According to some embodiments, variable aggregation may be used to
aggregate many automobile configurations that end up as being
equivalent. As an example only and not by way of limitation,
assuming among all constraints targeting SUVs, the constraints fall
into two categories: (A) constraints that target SUVs; and (B)
constraints that target black SUVs. It may then be possible to
eliminate some configurations of SUVs in the model. Therefore,
production planner 110 may create two variables (or declinations of
the FDVs): (1) the subset of all non-black SUVs; and (2) the subset
of all black SUVs. Therefore, for constraints relating to (A) SUVs,
production planner 110 uses variables (1) and (2) because this
represents the integration of all SUVs (black and non-black), and,
for constraints relating to (B) black SUVs, production planner 110
uses only variable (2). This provides for considering all
configurations of SUVs because the constraints are limited to only
that level of detail.
In other embodiments, if the constraints are defined in relation to
SUVs with options for the radio, transmission, sunroof, and other
like options, then production planner 110 would construct
additional declinations of FDVs for the additional subgroups of
SUVs, and the model would result in more variables. However, when
the constraints are limited to particular ODS, and even when there
are many of them, production planner 110 will work faster by
working with much less than the 1,000,000 FDVs, if they are not
aggregated.
The following examples illustrate construction of an automobile
supply chain network with automobile aggregation, according to an
embodiment.
FIG. 4 illustrates a graphical representation of a network model
400 of the automobile supply chain network, according to an
embodiment. According to embodiments, network model 400 represents
one or more relationships between variables and constraints using
nodes and arrows. For example, vehicle model 402 may be produced by
one or more manufacturing plants 404 for one or more markets 406
during a first planning period 408 and a second planning period
410. Each of the short arrows 412a-412l in the tree chart at the
top represents one instance of the variable x.sub.vehicle
model,plant,market,period, which represents the number of an
automobile model at a particular manufacturing plant for a
particular market in a particular time period. A second variable in
the model is the stock variable, S.sub.vehicle model,market,period.
The stock variable represents an amount of stock for an automobile
model at a time period for a particular market. In the tree chart,
the arrows 414a-414b represent the stock variable, which may
represent carryover stock that is carried over from one period to
another, such as from a first period 408 to a second period 410.
Each of the manufacturing plants 404 may have an initial stock
during a first period 408, which is represented by the diagonal
arrows 416a-416b. Each of the manufacturing plants 404 may also
have sales of automobiles during each period 408-410, which is
represented by the diagonal arrows 418a-418d.
The vehicle model 402 may represent an imprecise configuration of
an automobile, because the stock constraints are not completely
defined in the automobile. The vehicle model 402 may represent an
amount of an automobile class (such as a sedan) in a specific
market 406, but is not specific to which variant of sedan it is, or
the complete configuration of that automobile.
The following example illustrates graphically the generation of a
linear programming problem according to an embodiment using the
network model 400 of the production plan. By way of example only
and not by way of limitation, assume that the production is planned
for building a sedan vehicle model 402 for two time periods 408-410
(such as, for example, January and February) in two different
markets 406 (USA and Canada). Further assume that there are two
manufacturing plants 404, Plant A and Plant B. Plant A produces for
both markets 406 (USA and Canada), and Plant B produces only for
the Canadian market. Continuing with the example, assume the sedan
vehicle model 402 has three options, each with two choices for the
option, according to the following:
TABLE-US-00004 TABLE 4 Class: Sedan Option Choice 1 Choice 2 Engine
V8 4 cylinder Sunroof Yes No Spoiler Yes No
This gives eight possible configurations:
TABLE-US-00005 TABLE 5 Sedan, V8, spoiler Sedan, 4 cyl., spoiler
Sedan, V8, sunroof, spoiler Sedan, 4 cyl., spoiler, sunroof Sedan,
V8 Sedan, 4 cyl. Sedan, V8, sunroof Sedan, 4 cyl., sunroof
Because the example of FIG. 4 only focuses on the sedan, without
regard to the options, the vehicle model 402 can be represented
using only one automobile class in the model: a sedan with any
option. The total production of the sedan will then be split
between the eight possible automobile configurations in a post
process.
Assume further that the sales forecast, the minimum and maximum
inventory (Min-Max Stock Target), the initial inventory, and the
production capacity (in vehicles) are only on the sedan vehicle
model 402 for the January and February planning periods 408-410 in
the USA and Canadian markets 406, according to TABLES 6 and 7.
TABLE-US-00006 TABLE 6 January February USA Canada USA Canada Sales
Forecast 650 300 700 550 Min-Max Inventory Target 450-500 375-400
500-550 400-425 Initial Inventory 50 25 N/A N/A
TABLE-US-00007 TABLE 7 January February Manufacturer PLANT A PLANT
B PLANT A PLANT B Production Capacity 500 400 600 500
FIG. 5 illustrates the flow of production of a single automobile
model through the exemplary network model of FIG. 4 with various
constraints indicated, according to an embodiment. In the graphical
representation of a network model 400, sales for each market 404
for all sedan configurations are indicated by arrows 418a-418d
pointing outward from the ellipses representing the markets 404
(USA and CAN). Sales indicate the number of automobiles of all
vehicle models 402 sold for each market 406 in each time period
408-410. For example, sales for January for the USA market are
indicated as 650 automobiles, and for the Canadian market as 300
automobiles. Sales for February are 700 automobiles for the USA
market and 550 automobiles for the Canadian market.
In addition to sales, production capacity may also be added to the
model. Production capacity, represented by "Max" and a number
beneath each manufacturing plant 404, indicated by triangle labeled
A and B (representing Plant A and Plant B), identify the maximum
production capacity for all sedan configurations for each time
period 408-410 and for each manufacturing plant 406. For example,
the maximum capacity for Plant A to build all sedan configurations
is 500 automobiles in January and 600 automobiles in February.
Similarly, the maximum capacity for Plant B to build all sedan
configurations is 400 automobiles in January and 500 automobiles in
February.
In addition to production capacity, the minimum and maximum stock
for each market 408-410 and manufacturing plants 404 may be added
to the network model 400. Minimum and maximum stock, represented by
arrows 414a-414b indicate a numerical range for the minimum and
maximum number of automobiles that may be held in stock from one
period to the next. For example, the number of sedans of all
configurations that may be held in stock between January and
February for the USA market ranges between a minimum of 450
automobiles and a maximum of 500 automobiles. Similarly, the number
of sedans of all configurations that may be held in stock between
January and February for the Canadian market ranges between a
minimum of 375 automobiles and a maximum of 400 automobiles.
In addition to the minimum and maximum stock, initial stock for
each manufacturing plant 404 and market 406 may be added to the
model. Initial stock, indicated by arrow 416a-416b pointing to each
market 406 in the January time period, indicates the number of
automobiles that are held in stock at each manufacturing plant 404
or market 406 at the beginning of the planning horizon. For
example, the amount of sedans of all configurations that are
initially in stock for January in the USA market is 50 automobiles.
Similarly, the amount of sedans of all configurations that are
initially in stock for January in the Canadian market is 25
automobiles.
At this point, production planner 110 may generate a mixed integer
linear program that may solve for the number of sedans that are to
be built by each manufacturing plant 404 and shipped to each market
406 for each time period 408-410. After the one or more inputs
described above are received by production planner 110, production
planner 110 may utilize a MIP model to determine a production plan.
The MIP model may comprise the decision variable, x.sub.vpmt, the
volume of vehicle model 402 v .di-elect cons. V produced at
manufacturing plant 404 p .di-elect cons. P for market 406 m
.di-elect cons. M at time period 408-410 t .di-elect cons. T, and
the following constraints (1)-(3):
.times..times..function..times..di-elect
cons..times..times..times..times..times..A-inverted..di-elect
cons..times..di-elect
cons..times..ltoreq..times..times..times..times..times..times..A-inverted-
..di-elect cons..di-elect
cons..times..times..times..ltoreq..times..times..A-inverted..di-elect
cons. ##EQU00001##
where, L represents the vehicle model set, and ODS represents an
Option Data Set. The first constraint (1) of the MIP model depicted
above represents sales forecasts where the stock at the end of the
last period plus the production minus the stock at the end of the
first period equals the sales forecast. The sales from a specific
period may be shuffled to obtain what remains in stock so that the
equation may be represented by the previous stock added to what is
produced and subtracting what is sold is equal to the new stock for
a given period.
The second constraint (2) of the MIP model represents the
production capacity constraints. The number of vehicles of a model
(sedan or SUV for example) produced at a manufacturing plant 404 is
limited by the capacity of this manufacturing plant 404. These
constraints are defined for each manufacturing plant 404, each
period 408-410, and each vehicle model 402.
The third constraint (3) of the MIP model is based on an option
definition set (ODS). For each option definition set, there is a
supplier/production capacity constraint. These constraints are
defined for each option definition set and for each period.
In this MIP model, all constraints may be soft by adding under and
over slack variables. The slack variables may then be added into
the objective function and minimized with a descendant priority of:
sales forecast, production capacities, option capacities, and
initial stock. Although the MIP model is described with a
descendent priority, embodiments contemplate a database structure
that provides for reordering the priority of each variable or
constraint according to particular needs. For example, sales
forecast is currently indicated as having a highest priority.
However, by altering the formation of the database that stores the
sales forecasts, a different constraint may be placed in a higher
priority than the sales forecasts. In addition, or as an
alternative, in order to produce the most automobiles as possible,
a penalty may be associated with any result that produces less
automobiles than fixed by the MIP model.
Continuing with the same example above, assume that there is an
additional constraint comprising a sales forecast for a "sedan with
a spoiler" that is 90 sedans with spoilers in January and 100
sedans with spoilers in February. According to the exemplary
network model 400 that was just described, the automobile
represents all configurations of a sedan by a single vehicle model
402. Therefore, to add this constraint, the production planner 110
must split the single vehicle model 402 into two automobile
classes: sedans WITH a spoiler and sedans WITHOUT a spoiler,
according to the following:
TABLE-US-00008 TABLE 8 Class: Sedan With Spoiler Without Spoiler
Sedan, V8, spoiler Sedan, V8 Sedan, 4 cyl., spoiler Sedan, 4 cyl.
Sedan, V8, sunroof, spoiler Sedan, V8, sunroof Sedan, 4 cyl.,
spoiler, sunroof Sedan, 4 cyl., sunroof
With this new constraint that targets the option "spoiler," the
network model 400 must now make a distinction between a sedan with
a spoiler and a sedan without a spoiler. Four automobile
configurations (the ones with spoilers) will be linked to the
automobile "sedan with spoiler," and the other four automobile
configurations (the ones without spoilers) will be linked to the
automobile "sedan without spoiler." The total production of "sedan
with spoiler" will then be split between the four possible
automobile configurations with spoiler in a post process. The same
process will be done with the "sedan without spoiler." The
additional spoiler option production capacity constraint may be
added to the model as illustrated in the following figure.
FIG. 6 illustrates the flow of production of two automobile models
through the exemplary network model 400 of FIG. 4 with various
constraints indicated, according to an embodiment. To add the
additional option production capacity, the automobile that was
represented by a single automobile icon is now split into two
automobile icons 602-604: one that represents sedans with spoiler
602, and the other one that represents sedans without spoiler 604.
The market sales demand for the sedan with the spoiler indicated by
arrows 606a-606c may be placed on the arrows 412a-412c connecting
the sedan with spoiler icon 602 to the manufacturing plant 404 that
can produce the sedan with spoiler in the first period 408.
Similarly, the market sales demand for the sedan with the spoiler
indicated by arrows 606d-606f may be placed on the arrows 412m-412o
connecting the sedan with spoiler icon 602 to the manufacturing
plant 404 that can produce the sedan with spoiler in the second
period 410. Here, the number of sedans with spoiler is indicated as
a maximum of 90 automobiles for January, and a maximum of 100
automobiles for February.
Once all constraints are modeled, production planner 110 may
generate an optimization solution based on known amounts (i.e.
sales, production capacity, minimum and maximum stock, initial
stock, and/or option production capacity). Production planner 110
determines the flow on each production flow arrow 412a-412x in the
above graphic to satisfy the constraints arrows 414a-414b,
416a-416b, 418a-418d, and 606a-606f, while respecting the
conservation of flow (i.e. that the flow entering a node must equal
the flow leaving the node). To perform this determination,
production planner 110 transforms the chart into a linear program
and solves it with, for example, a simplex algorithm, according to
one or more mathematical models, as explained in detail above. By
way of a further example of a graphical network model 400 consider
the following.
FIG. 7 illustrates the flow of production of two automobile models
with three configurations through the exemplary network model 400
of FIG. 4 with various constraints indicated, according to an
embodiment. A first automobile configuration 702 is a sedan model
with a spoiler, the second automobile configuration 704 is a sedan
model with upgraded wheels, and the third vehicle configuration 706
is a hatchback model. The triangles represent automobile
manufacturing plants 404, the ellipses represent destination
markets 406, and the arrows 412a-412y connecting the automobile
configurations 702-706, manufacturing plants 404, and markets 406
represent the allocation of automobiles among the various
manufacturing plants 404 and markets 406. The tree chart is also
divided into two periods, a first period 408 (Period 1) on the left
side, and a second period 410 (Period 2) on the right side.
During the first period 408, Period 1, the arrow 412a connecting
the first automobile configuration 702 (sedan model with spoiler)
to Plant A represents that Plant A will produce 50 automobiles of
the first configuration. The arrow 412b connecting the first
automobile configuration 702 to Plant B represents that Plant B
will produce 70 automobiles of the first configuration. Similarly,
Plant A will produce 80 automobiles of the second configuration 704
(sedan model with upgraded wheels) and Plant C will produce 20
automobiles of the second configuration 704. Also, Plant B will
produce 100 automobiles of the third configuration 706
(hatchback).
The arrows 412f-412n connecting manufacturing plants 404 to markets
406 represent the automobile configurations shipped from those
manufacturing plants to the markets indicated. For example, the
uppermost arrow 412f connecting Plant A to the USA market indicates
that 20 automobiles of the first configuration 702 will be shipped
from Plant A to the USA market. Similarly, the network model 400
indicates that 30 automobiles of the first configuration 702 will
be shipped from Plant A to the Canadian market. Continuing with the
example, Plant B will produce 70 automobiles of the first
configuration 702 for the Canadian market and no automobiles of the
first configuration 702 for the USA market. Plant A will also
produce 50 automobiles of the second configuration 704 for the USA
market and 30 vehicles for the Canadian market. Plant C will
produce 10 automobiles of the second configuration 704 for the USA
market and also produce 10 automobiles for the Canadian market.
Finally, Plant B will produce 75 automobiles of the third
configuration 706 for the Canadian market and 25 automobiles for
the Mexican market.
Also, some amount of initial stock may be present in Period 1 for
the USA market for the first automobile configuration. This is
indicated by "Initial Stock" and an arrow 416a pointing at the USA
market for the first automobile configuration 702. The network
model 400 indicates that 50 automobiles were sold from this first
time period by arrow 418a, and the arrow 414a from the USA ellipse
in Period 1 to the USA ellipse in Period 2 represents unsold stock
(30 automobiles) that is carried over from Period 1 408 to Period 2
410.
For Period 2, a similar production plan is determined based on the
automobile configurations 702-706, manufacturing plants 404, and
the destination markets 406, and including any unsold stock from
Period 1. For example, in the second period 410, Period 2, 40
automobiles of the first automobile configuration 702 will be
produced by Plant A and 10 automobiles of the first automobile
configuration 702 will be produced by Plant B. Plant C will produce
20 automobiles of the second configuration 704, and Place B will
produce 80 automobiles of the third configuration. 706. Similar to
Period 1, production planner will determine automobile allocation
from each manufacturing plant 404 to different destination markets
406 based on particular automobile configurations 702-706. For
example, 20 automobiles of the first configuration 702 will be
shipped from Plant A to the USA, and 20 automobiles of the first
configuration will be shipped from Plant A to the Canadian market.
The allocation of the remaining automobiles is indicated. After all
constraints are input into the model, production planner 110 may
determine a production plan using the MIP model described and
comprising the number of automobiles to produce at particular
plants shipped to particular regions during particular time
periods.
Turning to a separate model, production planner 110 may determine a
production plan using a well-structured MIP model which comprises
three sub-models: a network production sub-model, an option
capacity sub-model, and a linking constraints sub-model.
The network production sub-model may comprise, for example, a
suitable mixed integer linear program model consistent according to
the following. For example, given the decision variables,
x.sub.lpmt, which is the volume of the automobile model l (l
.di-elect cons. L) (such a sedan, SUV, or other like automobile
models) produced at manufacturing plant p (p .di-elect cons. P) to
market m (m .di-elect cons. M) at period t (t .di-elect cons. T);
and w.sub.tpt/z.sub.tpt, which are the under/over slack of
production of automobile model l, for plant p at period t; and the
parameter c.sub.lpt/d.sub.lpt, which is the under/over penalty of
slack of production constraints of model l, for plant p at period
t, the production network model may comprise the following
objective (4) and constraints (5)-(9):
.times..times..di-elect cons..times..di-elect cons..times..di-elect
cons..times..times..times..times..times..times..times..times..times..time-
s..function..times..times..times..times..times..A-inverted..di-elect
cons..di-elect
cons..times..times..times..times..times..A-inverted..di-elect
cons..di-elect cons..di-elect cons.<.ltoreq..gtoreq..di-elect
cons. .times..times..times..times..times..times..times..times.
##EQU00002##
The objective (4) of the production network sub-model minimizes the
penalty associated with the slack to satisfy the sales forecast and
minimize the violation of the production capacity. The constraints
include the sales forecast (5) must be equal to the initial stock
at the beginning of a planning period, stock.sub.ml(t-1), plus the
production of the automobile model during the production period,
.SIGMA..sub.p.sup.P x.sub.lpmt, minus the stock remaining at the
end of the production period, stock.sub.mlt. A further constraint
(6) includes setting production capacity an automobile model equal
to the production at a particular plant, .SIGMA..sub.m .di-elect
cons.M x.sub.lpmt, plus or minus any slack, w.sub.lpt-z.sub.lpt.
Also, all stock, stock.sub.mlt, is constrained (7) to be between
the minimum stock, minStock.sub.mlt, and maximum stock,
maxStock.sub.mlt. Also, all decision variables are non-negative (8)
and natural numbers (i.e. zero and positive integers) (9).
Although the network production sub-model accounts for production,
sales forecast, and stock constraints, it fails to address option
capacity constraints. For example, continuing with the example of
FIGS. 4-5, assume that an additional constraint of a sales forecast
for a sedan with a spoiler is 90 automobiles in January and 100
automobiles in February. According to the exemplary network model
400 that was described, the network model 400 of FIGS. 4-5 fails to
account for different configurations of a sedan. For example, the
sedans may comprise the eight configurations of sedans WITH a
spoiler and sedans WITHOUT according to TABLE 8. Instead of
continuing with the network model 400 of FIG. 6, production planner
110 may employ an option capacity sub-model to model the
configurations of the vehicle model.
According to embodiments, the network model 400 does not
efficiently model automobile options because, owing to the large
number of possible configurations, the network model 400 may be too
large to calculate (the RAM requirements have exceeded 256 GB in
some models) for even a moderate-sized automobile supply chain
network 100. Therefore, even though production planner 110 may use
the production network sub-model to determine how many sedans may
be produced, it cannot determine automobile options, such as how
many sedans with spoilers should be produced and how many sedans
without spoilers should be produced.
Instead, production planner 110 models option capacity production
and FDV using the option capacity sub-model. Markets are not
targeted in capacity constraints so these may be merged in the
option capacity sub-model. In other words, the production network
sub-model considers only automobile models (such as, for example,
sedans and SUVs), and the option capacity sub-model considers the
FDV, such as, for example, particular sedan configurations,
including the sedan with particular configurations of spoilers,
engines, or sunroofs, as indicated in TABLE 8.
According to embodiments of the option capacity sub-model,
production planner 110 models option capacity production and FDV
with the decision variables, y.sub.vpt, which is the volume of the
vehicle (or class) v, (v .di-elect cons. V), produced at plant p (p
.di-elect cons. P), at period t (t .di-elect cons. T);
u.sub.ODSt/O.sub.ODSt, which are the under/over slack of capacity
ODS at period t; and parameters, a.sub.ODSt/b.sub.ODSt, which are
the penalties for the under/over slack of capacity ODS at period t.
Some FDVs cannot be produced in certain manufacturing plants. If an
automobile model v can be produced in manufacturing plant p, then,
this may indicated in the option constraint model by v .di-elect
cons. Vp. An automobile class may be any suitable configuration of
an automobile or automobile component, such as "sedan with a
spoiler," being one automobile class, and "sedan without a
spoiler," being another automobile class. Although particular
examples are given, automobile class may comprise any configuration
of automobile or automotive components, according to particular
needs.
According to embodiments, the option capacity sub-model comprises
the following objective (10) and constraints (11)-(13):
.times..times..di-elect cons..times..di-elect
cons..times..times..times..times..times..times..times..times..di-elect
cons..times..di-elect cons..di-elect
cons..times..times..times..A-inverted..di-elect
cons..times..times..A-inverted..gtoreq..di-elect cons.
.times..times..times..times..times..times..times..times.
##EQU00003##
The objective (10) of the option capacity sub-model minimizes the
violation of the capacity constraints for ODS by the penalties
associated with the slack. The constraints include the option
capacity constraint, capacity.sub.ODSt, (11), and that all decision
variables are non-negative (12) and natural numbers (13).
To generate a production plan, the production network sub-model and
the option capacity sub-model may be joined by the linking
constraints sub-model to generate a complete well-structured MIP
model. According to embodiments, the linking constraint
comprises:
.di-elect cons..times..di-elect
cons..times..times..times..A-inverted..di-elect cons..di-elect
cons..times..A-inverted. ##EQU00004##
The linking constraint sub-model comprises a constraint (14) that
joins the production network sub-model to the option capacity
sub-model by setting as equal to zero the difference between volume
of an automotive model (such as, for example, the sedan) that is
produced at plant p at market m, at time period t, x.sub.lpmt,
equal to the production of the automobile class (such as, for
example, "sedans with spoilers," and "sedans without spoilers) at
plant p, at period t, y.sub.vpt. Because market m is not taken into
account in the option capacity sub-model, the linking constraint
sums the volume of the automobile or automotive component over all
markets.
When the network production sub-model, the option capacity
sub-model, and the linking constraints sub-model are joined,
production planner may generate the following complete
well-structured MIP model of minimizing the objective function (15)
subject to constraints (16)-(22):
.times..times..di-elect cons..times..di-elect
cons..times..times..times..di-elect cons..times..di-elect
cons..times..di-elect
cons..times..times..times..times..times..times..times..times..times..time-
s..function..di-elect
cons..times..times..times..times..times..A-inverted..di-elect
cons..di-elect
cons..times..times..times..times..times..A-inverted..di-elect
cons..di-elect cons..di-elect cons..ltoreq..ltoreq..di-elect
cons..times..times..di-elect cons..di-elect
cons..times..times..times..A-inverted..di-elect
cons..times..times..A-inverted..di-elect cons..times..di-elect
cons..times..times..times..A-inverted..di-elect cons..di-elect
cons..times..times..A-inverted..gtoreq..di-elect cons.
##EQU00005##
The solution of the complete well-structured MIP model represents
the production plan for the automotive supply chain network
including the constraints for sales forecasts, production capacity,
minimum and maximum stock, and the capacity for particular options,
as explained above in connection with the production network
sub-model and the option capacity sub-model.
FIG. 8 illustrates a linear equation matrix 800 of the complete
well-structured MIP model of the automotive supply chain network,
according to an embodiment. The matrix of the linear equation
matrix (Ax=b) illustrates the production network sub-model with
side constraints with the production and the min-max stock
constraints, the linking constraints, and the option capacity
sub-model.
FIG. 9 illustrates the structure 900 of the linear equation matrix
800 of the complete well-structured MIP model of the automotive
supply chain network, according to an embodiment. As can be seen by
the non-zero values in the structure 900, the complete
well-structured MIP model is well-structured. Based on the stock,
production constraints, linking constraints, and the production of
the production network sub-model equal to the production of the
option capacity sub-model, the complete well-structured MIP model
comprises a well-structured model, as can be seen by the diagonal
shape of the matrix, the minimal rows, the sparseness, and only
having values of -1 and 1 in the matrix. Matrices with this
structure may be solved quickly with even a commercial solver, such
as, for example, a SIMPLEX solver. Additionally, the linear
relaxation is close to the integer solution since it is a network
model with side constraints, which may be solved easily, as
well.
In fact, the model is able to efficiently solve in approximately
twenty minutes, which is substantially less time than traditional
methods, with a 0.01% gap, an automobile supply chain production
problem comprising, approximately, 400,000 vehicles, 77 vehicle
models, 22 plants, 48 time periods, 725 regions, 4,608 production
constraints (model/plant/period), 309,000 sales forecasts
(model/region/period), and 120,192 option capacity constraints
(more than 10000 options).
To further illustrate the operation of production planner 110,
consider the following regional sales forecast.
TABLE-US-00009 TABLE 9 Model REGION Sep-15 Oct-15 Nov-15 Dec-15
Jan-16 Feb-16 Mar-16 SEDAN USA 7592 7400 7321 8681 4215 4881 6266
SEDAN CAN 14522 12067 13792 11644 15688 15203 19307 SEDAN MEX 12784
10372 9195 14316 8268 9966 14328 PICKUP USA 483 837 889 1243 196
327 436 PICKUP CAN 2516 3275 3629 2727 3346 3251 3949 PICKUP MEX
1605 1041 869 1016 1260 749 1416 ELEC USA 115 26 13 9 15 16 18 ELEC
CAN 955 1178 1197 1115 802 757 1130 ELEC MEX 852 895 960 1748 937
995 1200 SUV USA 9052 6893 7036 7072 6560 5866 4312 SUV CAN 11571
9009 9132 8543 10093 9741 13919 SUV MEX 7926 5940 5584 5858 4683
5739 7577
TABLE 9 represents a regional sales forecasts associated with a
particular model for a particular region, for particular time
periods. For example, a SEDAN model in the American region is
needed in the amounts of 7,592 for September 2015 and 7,400 in
October 2015. This input comprises a first constraint on the
model.
TABLE-US-00010 TABLE 10 AUGUST SEPTEMBER Plant Model 2015/W35
2015/W36 2015/W37 2015/W38 2015/W39 2015/W40 B SEDAN 27 120 150 150
150 90 C ELEC 61 374 445 446 483 270 C SEDAN 390 2264 2676 2682
2908 1620 D SUV 1152 4597 4990 6115 5655 3375 A SEDAN 768 4170 4992
4992 1664 2796 A PICKUP 213 1156 1392 1392 464 696
TABLE 10 illustrates plant production capacities for particular
models associated with particular time periods. Here, the SEDAN
model may only be produced by the plant B in the amount of 120 in
the 36th week of 2015. This input comprises a further constraint on
the model.
TABLE-US-00011 TABLE 11 Model REGION Sep-15 Oct-15 Nov-15 Dec-15
Jan-16 SEDAN USA 9307 9606 9422 5954 6658 SEDAN CAN 22535 24331
25510 27182 27546 SEDAN MEX 12941 14720 15865 11396 13581 PICKUP
USA 614 705 938 290 358 PICKUP CAN 6690 6784 6423 6591 6630 PICKUP
MEX 1558 1507 1725 1675 1441 ELEC USA 3 4 4 5 7 ELEC CAN 1658 1766
1564 1215 1239 ELEC MEX 1215 1496 1914 1253 1302 SUV USA 18053
17938 15289 12010 9922 SUV CAN 13811 14030 14405 14661 15694 SUV
MEX 5522 5507 5653 4590 5359
TABLE 11 illustrates a minimum stock associated with each model for
a particular region at particular time periods. For example, the
SEDAN model is limited to a minimum of 9,307 automobiles in the
American region for September of 2015, and 9,606 in October of
2015. This comprises a further constraint on the model.
TABLE-US-00012 TABLE 12 Model REGION Sep-15 Oct-15 Nov-15 Dec-15
Jan-16 Feb-16 SEDAN USA 11634 12008 11777 7443 8322 8978 SEDAN CAN
28169 30414 31887 33978 34433 34869 SEDAN MEX 16176 18400 19831
14245 16976 18604 PICKUP USA 767 881 1172 362 448 481 PICKUP CAN
8362 8480 8029 8239 8287 8540 PICKUP MEX 1948 1884 2156 2094 1801
2227 ELEC USA 4 5 5 6 9 8 ELEC CAN 2073 2207 1955 1519 1549 1778
ELEC MEX 1519 1870 2392 1566 1627 1636 SUV USA 22566 22422 19111
15012 12403 10617 SUV CAN 17264 17537 18006 18326 19617 20881 SUV
MEX 6902 6884 7066 5737 6699 7263
TABLE 12 illustrates a maximum stock associated with each model for
a particular region at particular time periods. For example, the
SEDAN model is limited to a maximum of 11,634 automobiles in the
American region for September of 2015, and 12,008 in October of
2015. This comprises a further constraint on the model.
TABLE-US-00013 TABLE 13 Model REGION Total SEDAN USA 10176 SEDAN
CAN 20463 SEDAN MEX 9726 PICKUP USA 444 PICKUP CAN 5731 PICKUP MEX
1561 ELEC USA 95 ELEC CAN 1061 ELEC MEX 690 SUV USA 16267 SUV CAN
15945 SUV MEX 8080
TABLE 13 illustrates an initial stock associated with each model
for a particular region at the beginning of the production planning
period. The amount of automobiles indicated in the total column
represents the amount of initial stock on-hand at the particular
region at the beginning of the production planning period. For
example, the amount of SEDAN models in the American region at the
beginning of the planning period is 10,176 automobiles.
TABLE-US-00014 TABLE 14 2015/ 2015/ 2015/ 2015/ 2015/ ODS 2015/W36
W37 W38 W39 W40 W41 Black SUV with V8 1327 1164 1406 1300 1406 1300
Any model with V6 4617 4049 4892 4524 4892 4524 Sedan with a
sunroof 1616 911 1100 1017 1100 1017 but without spoiler SUV with a
radio A 1750 1306 1630 1750 1058 1600 All sedans and pickups 652
1000 1000 175 1025 650 SUV with a 4 cylinders 3800 3361 3240 3122
3323 3362
TABLE 14 illustrates constraints for ODS associated with particular
time periods. As explained in detail above, these constraints may
comprise production capacity constraints or other limits on the
total number of comprising FDV that match the ODS listed in the
above chart. For example, the total number of Black SUVs with V8 is
1,327 in the 36th week of 2015 and 1,164 in the 37th week of 2015.
After all constraints are input into the model, as explained in
greater detail above, production planner 110 may determine a
production plan comprising the number of automobiles to produce at
particular plants shipped to particular regions during particular
time periods.
The following table illustrates a simplified production plan for an
SUV model.
TABLE-US-00015 TABLE 15 Plant Market Period Volume A USA 1 15 A USA
2 20 B USA 1 0 C CAN 1 76 C CAN 2 112 C MEX 3 34
TABLE 15 illustrates an exemplary production plan for an automobile
configuration according to an embodiment. A production plan for the
automobile configuration FDV1 is illustrated. The FDV1 may
represent for example, a SUV model, a V8-4.2 engine, a RADA radio,
no all-wheel drive (AWD), and a black color. The production plan
generated by production planner 110 may indicate that the FDV1
model should be built in particular volumes, at particular plants,
in particular markets, for particular periods based on overall
sales volume forecast while respecting plant and supplier capacity.
For example, the production plan illustrated in the above figure
indicates that Plant A should product a volume of 15 automobiles
for the USA market in a first period, and 20 automobiles in a
second period. Plant B should product no automobiles, and Plant C
should produce 76 automobiles for the Canadian market in the first
time period, and 112 automobiles for the Canadian market in the
second time period. Plant C should also produce 34 automobiles in a
third period for the Mexican market. Although particular plants,
markets, periods, and volumes are illustrated, embodiments
contemplate any suitable number or types of plants, markets,
periods, or volumes according to particular needs.
The production plan based on the FDV1 automobile configuration is
simpler than previous attempts at generating a production plan.
According to embodiments, the solution does not target all the
options in an automobile configuration. Many of the options will
have been aggregated together through declination of FDVs, as
described above.
Reference in the foregoing specification to "one embodiment", "an
embodiment", or "some embodiments" means that a particular feature,
structure, or characteristic described in connection with the
embodiment is included in at least one embodiment of the invention.
The appearances of the phrase "in one embodiment" in various places
in the specification are not necessarily all referring to the same
embodiment.
While the exemplary embodiments have been shown and described, it
will be understood that various changes and modifications to the
foregoing embodiments may become apparent to those skilled in the
art without departing from the spirit and scope of the present
invention.
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